| This dissertation studies the theoretical underpinnings of active sonar classifiers. We present a systematic approach for designing optimal Bayesian classifiers and analyzing their performance. We emphasize the ternary case where three hypotheses are considered: ;We start by deriving a sufficient statistic to decide between ;Many design parameters affecting classifier performance are studied. Perhaps the most important issue is classifier performance when incorrect a priori knowledge of the target's spatial properties is processed. Other parameters such as target resolution, signal-to-noise ratio, transmitter constant in linear FM signals, etc. are investigated as well.;The final issue presented is acoustic target imaging. A minimum variance linear unbiased estimator of the scattering coefficients of the test volume encompassing the target is derived. Furthermore, we investigate error minimization of the MVLU estimator in terms of system characteristics such as array and/or signal design. We also discuss the relation between classification and imaging.;In summary, ideas from decision theory, detection and estimation theory are combined in order to implement optimal Bayesian classifiers and acoustic imagers. |
